A known example of an art for generating a restored image from a degraded image includes a super resolution art. Among the super resolution arts, a method using a dictionary is especially referred to as a learning-type super resolution art. This dictionary is a dictionary generated by learning cases where a low resolution image and a high resolution image are associated with each other.
An example of a learning-type super resolution art is described in NPL 1. In the learning-type super resolution art described in NPL 1, the following scheme (hereinafter referred to as a super resolution process) is executed.
First, in the super resolution process, an input image which is a low resolution image is received.
Subsequently, in the super resolution process, a low frequency component is generated from the input image.
Subsequently, in the super resolution process, a low frequency patch is cut out from the generated low frequency component, and a low frequency feature amount is calculated from the low frequency patch.
Subsequently, in the super resolution process, multiple pieces of low frequency feature amount learning data are searched from the dictionary in the ascending order of the distance from the calculated low frequency feature amount. Then, in the super resolution process, high frequency feature amounts which form pairs with the searched low frequency feature amount learning data are read out.
Subsequently, in the super resolution process, a single high frequency feature amount is selected on the basis of the distance during the searching, inconsistency with an adjacent high frequency block, a co-occurrence probability of a low frequency feature amount and a high frequency feature amount learned separately in a learning stage, and the like.
For example, PTL 1 discloses an example of an image processing device.
In the image processing device of PTL 1, a parameter selection unit selects a combination of multiple different image processing parameters on the basis of an attribute of a subject identified from an input image. Subsequently, an image generation unit uses these selected image processing parameters to improve the image quality of the subject included in the input image. Then, the image generation unit selects at least a single image on the basis of comparison with the input image from among multiple images obtained from the image quality improvement, and makes the selected image into a high image quality image.